# 导入需要用到的package
import os

import matplotlib.pyplot as plt
import numpy as np

os.getcwd()


def load_data():
	# 从文件导入数据
	# datafile = './work/housing.data'
	data = np.fromfile(datafile, sep=' ')

	# 每条数据包括14项，其中前面13项是影响因素，第14项是相应的房屋价格中位数
	feature_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', \
	                 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
	feature_num = len(feature_names)

	# 将原始数据进行Reshape，变成[N, 14]这样的形状
	data = data.reshape([data.shape[0] // feature_num, feature_num])

	# 将原数据集拆分成训练集和测试集
	# 这里使用80%的数据做训练，20%的数据做测试
	# 测试集和训练集必须是没有交集的
	ratio = 0.8
	offset = int(data.shape[0] * ratio)
	training_data = data[:offset]

	# 计算train数据集的最大值，最小值，平均值
	maximums, minimums, avgs = training_data.max(axis=0), training_data.min(axis=0), \
	                           training_data.sum(axis=0) / training_data.shape[0]

	# 对数据进行归一化处理
	for i in range(feature_num):
		# print(maximums[i], minimums[i], avgs[i])
		data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i])

	# 训练集和测试集的划分比例
	training_data = data[:offset]
	test_data = data[offset:]
	return training_data, test_data


class Network(object):
	def __init__(self, num_of_weights):
		# 随机产生w的初始值
		# 为了保持程序每次运行结果的一致性，此处设置固定的随机数种子
		# np.random.seed(0)
		self.w = np.random.randn(num_of_weights, 1)
		self.b = 0.

	def forward(self, x):
		z = np.dot(x, self.w) + self.b
		return z

	def loss(self, z, y):
		error = z - y
		num_samples = error.shape[0]
		cost = error * error
		cost = np.sum(cost) / num_samples
		return cost

	def gradient(self, x, y):
		z = self.forward(x)
		N = x.shape[0]
		gradient_w = 1. / N * np.sum((z - y) * x, axis=0)
		gradient_w = gradient_w[:, np.newaxis]
		gradient_b = 1. / N * np.sum(z - y)
		return gradient_w, gradient_b

	def update(self, gradient_w, gradient_b, eta=0.01):
		self.w = self.w - eta * gradient_w
		self.b = self.b - eta * gradient_b

	def train(self, training_data, num_epoches, batch_size=10, eta=0.01):
		n = len(training_data)
		losses = []
		for epoch_id in range(num_epoches):
			# 在每轮迭代开始之前，将训练数据的顺序随机的打乱，
			# 然后再按每次取batch_size条数据的方式取出
			np.random.shuffle(training_data)
			# 将训练数据进行拆分，每个mini_batch包含batch_size条的数据
			mini_batches = [training_data[k:k + batch_size] for k in range(0, n, batch_size)]
			for iter_id, mini_batch in enumerate(mini_batches):
				# print(self.w.shape)
				# print(self.b)
				x = mini_batch[:, :-1]
				y = mini_batch[:, -1:]
				a = self.forward(x)
				loss = self.loss(a, y)
				gradient_w, gradient_b = self.gradient(x, y)
				self.update(gradient_w, gradient_b, eta)
				losses.append(loss)
				print('Epoch {:3d} / iter {:3d}, loss = {:.4f}'.
				      format(epoch_id, iter_id, loss))

		return losses


if __name__ == '__main__':
	datapath = os.getcwd() + r'/learn/paddle_learn/housing_forecast/'
	# 读入训练数据
	# datafile = datapath + 'data/housing.data'
	datafile = 'data/housing.data'

	# 获取数据
	train_data, test_data = load_data()

	# 创建网络
	net = Network(13)
	# 启动训练
	losses = net.train(train_data, num_epoches=50, batch_size=100, eta=0.1)

	# 画出损失函数的变化趋势
	plot_x = np.arange(len(losses))
	plot_y = np.array(losses)
	plt.plot(plot_x, plot_y)
	plt.show()
